Recommendation platform for skill development

ABSTRACT

Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/191,047 filed Mar. 3, 2023, which is a continuation application ofU.S. patent application Ser. No. 17/159,015, titled “RECOMMENDATIONPLATFORM FOR SKILL DEVELOPMENT,” filed Jan. 26, 2020, which claimspriority to U.S. Provisional Application No. 63/017,243, titled“RECOMMENDATION PLATFORM FOR SKILL DEVELOPMENT,” and filed Apr. 29,2020; which is incorporated by reference hereto.

TECHNICAL FIELD

The disclosure relates to identification and allocation of skills. Moreparticularly, the disclosure relates to management of qualifications intemporary staffing positions.

BACKGROUND

Traditionally, temporary employment staffing systems have includedbranch offices where potential workers arrive early in the morning andare directed to various available temporary staffing positions for theday (e.g., event and convention workers, construction, skilled laborers,one-time projects, etc.) based on their experience.

The above staffing model has evolved into a digital model that makes useof mobile applications to guide potential workers to availablepositions.

Additionally, when an employer wishes to identify potential employeesfor a job that needs to be filled, it is common that the employer willplace a job advertisement with a job classified website or the like.However, the employer must then filter through the applications frompotential employees which can be a tedious task. Another commontechnique is that the employer may engage a recruiter to identifypotential employees. The more traditional staffing method requiressignificant investment, retaining workers for long-term positions, anddoes not enable quick scale-up or scale-down of work force.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example of a particular embodiment of the present inventioncan be realized using a processing device.

FIG. 2 illustrates a networked communications system that may includethe processing device.

FIG. 3 illustrates a system diagram of a system for matching workers toentities which define jobs.

FIG. 4 is a flowchart illustrating a method of provisionally matchingworkers to job with near-miss requirements.

FIG. 5 is a flowchart illustrating a verification of skills. Users mayhave different skills ratings for different types of work.

FIG. 6 is a flow chart illustrating a process of indicating avenues toqualify for provisional matches.

FIG. 7 is a flowchart illustrating generation of a potential career pathdevelopmental plan.

FIG. 8 is a flowchart illustrating a process of automatically generatinga resume for workers on the temporary employment platform.

FIG. 9 is a flowchart illustrating a process of gamification of platformengagement.

FIG. 10 is a flowchart illustrating issuance of gamified rewards relatedto badges.

DETAILED DESCRIPTION

Short-term, temporary employment staffing platforms operate by linking anumber of available workers to gigs (e.g., short-term, temporaryemployment). Available jobs are matched to workers and recommendedthereto. The matching process is largely based on qualifications of theworker based on their previous work, certifications, review on previousgigs, and availability.

An example of a gig staffing platform makes use of a mobile deviceapplication where workers can browse their matches and sign up to work.Once the worker has chosen a job or gig and signs up, the worker showsup and works the gig. Because the positions are temporary (e.g., manylasting no more than a single shift), there does not tend to be any sortof extended evaluation or interview process. If a worker is qualified tosign up for the work, they may sign up and show up to the job. If theworker had worked for a given employer before, there may be apre-existing evaluation on that worker (e.g., blacklisting orwhitelisting the worker).

Once the worker performs the agreed upon work, an administrator at thegig employment reviews the worker and the worker repeats the processbased on a new rating received from the gig administrator.

In many cases, the available jobs have requirements. The requirementsvary from certifications, worker skills, worker previous experience,worker ratings, or other known suitable forms of temporary workerevaluations. If a worker does not fit the requirements, they will not bematched, and those jobs will not be available for a worker to browse.

In a system where workers are matched only to jobs where they fit therequirements, there is no encouragement for personal growth of the userbase. It is often the case that jobs requiring more requirements orcertifications pay better. Those users who use the system for extendedor consistent periods, benefit if they improve the positions for whichthey may sign up.

Disclosed herein is a system to introduce provisional job matches tousers of the temporary staffing platform, generate an automated resumefor the user, and gamify platform participation. Provisional job matchesare those jobs that would match but-for limited set of additionalrequirements not currently met by the worker.

In some embodiments, provisional job matches are a guided process thatmakes use of categorical skill sets that are shared between varioustypes of employment. For example, if a worker has experience workingwith heavy machinery, provisional job matches for that worker may bedirected to better positions that similarly are connected withmachinery.

Workers are shown a map view of available and provisional jobs matchedon percentage of compatibility characteristics between the workers'qualifications and preferences and the employers' service needs andpreferences. Worker qualifications and preferences will include but notbe limited to skills, industry, type of work, location, environment,assignment length, and/or potential earnings.

Associates will have the opportunity to earn badges through work qualityand tenure that will allow them to see more highly valued jobs. The samefactors used to show workers matching jobs are used on employer side toshow matching associates.

In some embodiments, workers are provided with a path to improve theirratings, or to obtain skills they would need to match with higherquality temporary positions. The path to obtain additional skills mayinvolve volunteer work to gain experience for the higher qualityposition or include directions toward obtaining necessary certifications(ex: a welding certification, a drug-free certification, or a class Adriver's license).

Exemplary System Embodiment

FIG. 1 is an example of a particular embodiment of the present inventioncan be realized using a processing device. In particular, the processingdevice 100 generally includes at least one processor 102, or processingunit or plurality of processors, memory 104, at least one input device106, and at least one output device 108, coupled together via a bus orgroup of buses 110. In certain embodiments, input device 106 and outputdevice 108 could be the same device. An interface 112 can also beprovided for coupling the processing device 100 to one or moreperipheral devices, for example interface 112 could be a PCI card or PCcard.

At least one storage device 114 which houses at least one database 116can also be provided. The memory 104 can be any form of memory device,for example, volatile or non-volatile memory, solid state storagedevices, magnetic devices, etc. The processor 102 could include morethan one distinct processing device, for example to handle differentfunctions within the processing device 100.

In alternative embodiments, the processing device 100 operates as astandalone device or may be connected (networked) to other machines. Ina networked deployment, the machine may operate in the capacity of aserver or a client machine in a client-server network environment, or asa peer machine in a peer-to-peer (or distributed) network environment.

Input device 106 receives input data 118 (such as electronic contentdata), for example via a network or from a local storage device. Outputdevice 108 produces or generates output data 120 (such as viewablecontent) and can include, for example, a display device or monitor inwhich case output data 120 is visual, a printer in which case outputdata 120 is printed, a port for example a USB port, a peripheralcomponent adaptor, a data transmitter or antenna such as a modern orwireless network adaptor, etc. Output data 120 could be distinct andderived from different output devices, for example a visual display on amonitor in conjunction with data transmitted to a network. A user couldview data output, or an interpretation of the data output, on, forexample, a monitor or using a printer. The storage device 114 can be anyform of data or information storage means, for example, volatile ornon-volatile memory, solid state storage devices, magnetic devices, etc.

Examples of electronic data storage devices 114 can include diskstorage, optical discs, such as CD, DVD, Blu-ray Disc, flashmemory/memory card (e.g., solid state semiconductor memory), MultiMediaCard. USB sticks or keys, flash drives. Secure Digital (SD) cards,microSD cards, miniSD cards, SDHC cards, miniSDSC cards, solid statedrives, and the like.

In use, the processing device 100 is adapted to allow data orinformation to be stored in and/or retrieved from, via wired or wirelesscommunication means, the at least one database 116. The interface 112may allow wired and/or wireless communication between the processingunit 102 and peripheral components that may serve a specialized purpose.The processor 102 receives instructions as input data 118 via inputdevice 106 and can display processed results or other output to a userby utilizing output device 108. More than one input device 106 and/oroutput device 108 can be provided. It should be appreciated that theprocessing device 100 may be any form of terminal. PC, laptop, notebook,tablet, smart phone, specialized hardware, or the like.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone or smart phone, a tablet computer,a personal computer, a web appliance, a point-of-sale device, a networkrouter, switch, or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable (storage) medium is shown in an exemplaryembodiment to be a single medium, the term “machine-readable (storage)medium” should be taken to include a single medium or multiple media (acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” or “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing,encoding, or carrying a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module, or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processors in a computer, cause the computerto perform operations to execute elements involving the various aspectsof the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine or computer-readable media include, but arenot limited to, recordable type media such as volatile and non-volatilememory devices, floppy and other removable disks, hard disk drives,optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), DigitalVersatile Discs, (DVDs), etc.), among others, and transmission typemedia such as digital and analog communication links.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number, respectively. The word “or,” in reference toa list of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

FIG. 2 illustrates a networked communications system 200 that mayinclude the processing device 100. Processing device 100 could connectto network 202, for example the Internet or a WAN. Input data 118 andoutput data 120 could be communicated to other devices via network 202.Other terminals, for example, thin client 204, further processingsystems 206 and 208, notebook computer 210, mainframe computer 212, PDA214, pen-based computer 216, server 218, etc., can be connected tonetwork 202. A large variety of other types of terminals orconfigurations could be utilized. The transfer of information and/ordata over network 202 can be achieved using wired communications means220 or wireless communications means 222. Server 218 can facilitate thetransfer of data between network 202 and one or more databases 224.Server 218 and one or more databases 224 provide an example of aninformation source.

Other networks may communicate with network 202. For example,telecommunications network 230 could facilitate the transfer of databetween network 202 and mobile or cellular telephone 232 or a PDA-typedevice 234, by utilizing wireless communication means 236 andreceiving/transmitting station 238. Mobile telephone 232 devices mayload software (client) that communicates with a backend server 206, 212,218 that operates a backend version of the software. The software clientmay also execute on other devices 204, 206, 208, and 210. Client usersmay come in multiple user classes such as worker users and/or employerusers.

Satellite communications network 240 could communicate with satellitesignal receiver 242 which receives data signals from satellite 244 whichin turn is in remote communication with satellite signal transmitter246. Terminals, for example further processing system 248, notebookcomputer 250, or satellite telephone 252, can thereby communicate withnetwork 202. A local network 260, which for example may be a privatenetwork, LAN, etc., may also be connected to network 202. For example,network 202 may relate to ethernet 262 which connects terminals 264,server 266 which controls the transfer of data to and/or from database268, and printer 270. Various other types of networks could be utilized.

The processing device 100 is adapted to communicate with otherterminals, for example further processing systems 206, 208, by sendingand receiving data, 118, 120, to and from the network 202, therebyfacilitating possible communication with other components of thenetworked communications system 200.

Thus, for example, the networks 202, 230, 240 may form part of, or beconnected to, the Internet, in which case, the terminals 206, 212, 218,for example, may be web servers, Internet terminals or the like. Thenetworks 202, 230, 240, 260 may be or form part of other communicationnetworks, such as LAN, WAN, ethernet, token ring, FDDI ring, star, etc.,networks, or mobile telephone networks, such as GSM, CDMA, 3G, 4G, etc.,networks, and may be wholly or partially wired, including for exampleoptical fiber, or wireless networks, depending on a particularimplementation.

FIG. 3 illustrates a system diagram of a system 300 for matching workersto entities which define jobs. In particular, the system 300 includes aserver processing system 310 in data communication with a first andsecond mobile device 370, 371, preferably smart phones, or tabletprocessing systems, etc., via a one or more communication networks. Thefirst mobile device 370 is operated by a worker and the second mobiledevice 371 is operated by an entity. It will be appreciated that thesystem 310 can include a plurality of first and second mobile devices370, 371 operated by a respective plurality of workers and entities. Theserver processing system 310 may access or include a data store 352including a user profile database 360 and a job database 350.

It will be appreciated that user profile database 360 and job database350 can be hosted by the server processing system 310; however, it isequally possible that the user profile database 360 and the job database350 are hosted by other database serving processing systems. Processingsystem 100 is suitable for operation as the server processing system310. The server processing system 310 includes a matching engine 320, alearned profile engine 330, and an aggregation module 340 which will bediscussed in more detail in various examples below.

The user profile database 360 includes profiles for both workers(associates) and employers (clients). When an employer user has aservice request (may be referred to as any of “job,” “shift,” or “gig”)the employer user makes use of the platform to select a job templatethat most closely matches the service request that they have andprovides the requisite time period the service request is associatedwith. Each job template includes a number of requisites(skills/certifications) for worker users to match to the job. Theemployer may add additional criteria to the service request in additionto the template (e.g., drug tests, average worker rating, etc.). Workerusers whom match the service request may sign up for the shift and workthat service request.

The matching engine 320 may match workers to job requests on an absoluteor percentage basis. Where a percentage basis is implemented, athreshold percentage is considered a match. A near-miss match may beestablished via an absolute basis or percentage. Where a percentagebasis is implemented, near-miss matches similarly use a thresholdpercentage, but lower than the threshold percentage for matches.

The mobile devices 370, 371 include a processor, a memory, an input andoutput device preferably provided in the form of a touch screeninterface, and a communication device. Preferably, the mobile device.370, 371 includes a location receiver (such as a Global PositioningSystem location receiver) 375. Preferably, the mobile devices 370, 371have stored in the memory a mobile device application 380 which can bedownloaded by the mobile devices 370, 371 from a software repositoryprocessing system. The user can register with the server processingsystem 310 as a worker or an entity. If the user registers as a worker,a worker interface 382 will be presented via the mobile application 380via their respective mobile device 370. If the user registers as anentity, an entity interface 384 will be presented via the mobileapplication 380 via their respective mobile device 371. However, it willbe appreciated that two separate mobile applications could be providedfor the two different types of users in alternate arrangements.

Provisional Recommendations

Worker users are shown a view (e.g., a map view) of available andprovisional (near-miss) jobs matched on percentage of compatibilitycharacteristics between the associates' qualifications and preferencesand the employer user service needs and preferences. Worker userqualifications and preferences will include but not be limited toskills, industry, type of work, location, environment, assignmentlength, potential earnings. Map view of jobs is filterable based onworker user preferences to include closest physical placement branchoffice. Worker users have the opportunity to earn badges through workquality and tenure that will allow them to see more highly valued jobs.The same factors used to show worker users matching jobs are used onemployer user side to show matching workers.

FIG. 4 is a flowchart illustrating a method of provisionally matchingworkers to job with near-miss requirements. In step 402, the systemmaintains a worker profile. The worker profile may be a new profile, ora pre-existing profile. Maintaining the profile includes a continuous,objective characterization of the workers' experience (previous gigsworked, experience external to the system), preferences (e.g., hoursavailability, distance of willing travel, location, or other suitableworker preferences), certifications, and previous reviews by employers(e.g., employers within the gig system providing post-gig reviews of aworker).

A worker's experience that occurs while operating within the system istagged with a number of skills associated with each gig worked (e.g.,use of machinery, customer service, construction, hospitality skills,etc.). In some embodiments, the available jobs/gigs in the system havepredetermined characteristics. When the job is listed on the system, thedetails of the job come from a template. The template includes themetadata tags indicating the skills involved. In some embodiments, thetemplates are modified or are flexible such that the listing employermay request specific additional skills or indicate that certain skillswill be used during the gig.

The tags associated with a given position are kept honest within thesystem at least based on worker feedback. Where more skills are requiredby the employer for a gig, the gig worker (and the placement system) maybe paid more. Where the worker accepts and works a job that wasmiss-tagged, that worker may lodge a notice with the system indicatingthe discrepancy.

Worker experience is tallied based on each gig worked. Each time a givenskill tag is associated with a gig a given worker works, the worker'stracked “proficiency” for that skill/tag increases within the system. Insome job postings, a predetermined or selected proficiency is requiredby the entity listing the position. After obtaining a predeterminedamount of experience in a given skill, the worker profile may have apublic badge indicating progression in a given skill level (e.g.,apprentice, journeyman, master). The badges may be used as requirementsor engender confidence in employer users.

Embodiments of reviews by past employers include star rankings, and/orshort text reviews. In some embodiments, maintenance of a worker profileincludes natural language processing (NLP) of the text reviews. The NLPprocess identifies common phrases and/or words used in reviews toperform sentiment analysis. In some embodiments, positive or negativereviews may affect a worker profile's proficiency statistics.

Generally speaking, there is a direct correlation between the number ofrequirements for a job listing and the wage/cost of a worker to fill thejob. Simple economic incentive encourages workers to accrue more skillsor better statistics in their maintained profiles.

In step 404, the system compares worker profiles to available positionsto identify near-misses. In many cases, workers are matched to positionsand the workers are enabled to work those positions and developadditional skills. In some circumstances, the system identifiesnear-misses. Near-misses do not necessarily evaluate each criteria of amatch evenly. Some criteria may be harder to change. For example, if aworker has a negative rating from the employer of a given listing, butperfectly matches on every other criterion, that worker may still not bea “near-miss” because the negative rating is given greater weight thatother criteria. Depending on embodiment, each criterion maintained on aworker's profile may be weighted differently with respect to whether aposition is considered a near-miss. In some embodiments, matches areevaluated on a percentage basis, and near-miss matches require a lowerthreshold percentage matching than a job match.

Where a former employer's rating may prevent a worker from matching ornear-miss matching, in some embodiments, there are intermediarybehavioral status indicators. Examples include use of NLP evaluations oftext reviews of a worker to extract and identify behavioral issues witha worker (e.g., the worker is perennially late). A near-miss may bebased on the worker not meeting a behavioral requirement as verified byNLP analysis of text reviews of worker performance.

In some embodiments, high skill in one skill tag may translate to lowerskill in another tag, and the skill translation may be used to establisha near-miss match. For example, if the worker has experience working inmanufacturing on a production line, they may be capable of working in awarehouse to pick and pack product. Example objective criteria forestablishing a near-miss may include worker profile matches to availablejob positions that include all necessary requirements except:

-   -   A. A certification (including employer specific requirements)    -   B. A required skill, while having a transferable skill    -   C. Sufficient proficiency in a required skill, while having at        least a threshold proficiency in the skill relative to the        requirement (e.g., based on required proficiency, the threshold        amount may still be zero)    -   D. Willingness to travel as far as necessary, while still        willing to travel a threshold distance relative to the        requirement (e.g., based on required distance, the threshold        amount may be de minimus)    -   E. Hours of availability    -   F. Meet a behavioral rating    -   G. Drug Tests    -   H. Background Checks

In step 406, the system forwards near-miss matches to the workers basedon a search rank. Provisional recommendations for jobs are most usefulto potential workers when developing toward those provisional positionsis an improvement over a status quo for that worker. In someembodiments, the near-miss positions are presented to the workerssorted/ranked by available wages as compared to past wages earned byeach worker.

In some embodiments, for each near-miss position matched to a worker,the posted wages for that near-miss position are compared to an averagethat worker has earned across other positions they have accepted/worked.In some embodiments, each near-miss position is compared to the highestwage earned by that worker from other positions. Other criteria may befurther be applied to refine near-miss results. For example, the wagesfrom the worker's previous positions may be limited by removingoutliers, or by only using data from a recent period of time (last week,last month, last quarter, last year, etc.).

Each forwarded near-miss position includes a reference to the criterionthat the individual worker is missing. In some embodiments, the displayincluding near-miss positions is on a different screen/“pane-of-glass”than where proper matches are displayed.

In step 408, in response to a worker user selecting a near-missposition, the system generates a plan for the worker to correct themissing criteria in order to shift the near-miss into a match. The planto correct is based on a style and/or magnitude of the missing criteria.In some circumstances, the plan may require only that the worker updatetheir preferences. In other circumstances, the plan may require that theworker sign up/work a particular set of gigs (that may be sub-optimal)to develop requisite skill proficiency/experience. In still othercircumstances, the plan may include information regarding the processrequired to obtain a requisite certification.

Where the missing criterion is behaviorally related, the plan may beconnected to working other gigs to an extent that reviews of previousgigs are overwritten/pushed out by new reviews. Text reviews (forpurposes of behavioral evaluation) may expire or be overwritten overtime. The system may further engage an observational program thatmonitors the worker's location and provides reminders as to when theworker should leave home to arrive on time, include friendly remindersconcerning how the worker should address peers and supervisors (e.g.,reminders about shaking hands, eye contact, not smoking on the job), howthey should dress, reminders on what equipment (e.g., personalprotective equipment) they should arrive with, and where they can obtainthe necessary equipment. In some embodiments, a more general set ofreminders related to an accepted position are delivered to usersregardless of whether their reviews have referenced behavioral issues.In some embodiments, the reminders that are delivered to worker userswith identified behavioral issues are tailored specifically to thebehavior issues that were identified via NLP of text reviews.

FIG. 5 is a flowchart illustrating a verification of skills. Users mayhave different skills ratings for different types of work, and someexperience comes from outside of service requests/gigs/shifts that arerecorded on the platform. In step 502, when a worker user is initiallyonboarded to the platform, the user indicates their past experience. Instep 504, the platform identifies a most-comparable template position(including skill tags). The identification may be performed via anin-person human decision, or via a machine learning model and/or aheuristic-based model. When performed by a machine learning model, atraining set of data comprises descriptions of various jobs, and thensubsequently forced into one of the available templates. When a new jobdescription is received by a user, the model similarly forces the newdescription into one of the platform job templates. Heuristics operatesimilarly to the machine learning model but make use of the presence ofkeywords in the present job description in order to associate with atemplate.

In step 506, the platform credits the worker user with a correspondingamount of skill in each of the skill tags associated with the templatebased on the length of time the worker user held the described position.

FIG. 6 is a flow chart illustrating a process of indicating avenues toqualify for provisional matches. In step 602, the platform identifies anear-miss criteria for a worker compared to a particular job/serviceneed. The type is identified based on a field from which the criteria isfound in the worker user's profile. The type of near-miss criteriaaffects the following actions that are taken by the platform. A numberof example categories that the near-miss criteria may fall into include:skill-based, preference-based, certification-based, or behavior-based.

In step 604, where the near-miss criterion is skill-based, the platformsuggests a set of positions that the worker user could sign up for thatwould develop the skills the worker would need to shift the near-missinto a match. In some embodiments, the necessary path may not beavailable on the platform, and additional notifications are delivered tothe user over a period of time as shifts become available to obtain thenecessary work experience. Examples include agreeing to take on gigs forreduced or no pay. The platform graphic user interface for the workerincludes controls for the worker to make concessions in order toincentivize an employer user to accept the worker user on the gig.

In step 606, where the near-miss criterion is preference-based, theplatform suggests that the worker user go into their profile and makemodifications that resolve the issue.

In step 608, where the near-miss criterion is certification-based, theplatform either connects the worker user to an agent to provide guidanceon where the worker may obtain the necessary certification or provides aweblink to a written guide.

In step 610, where the near-miss criterion is behavior-based, theplatform first informs the user of the behavior issue (e.g., the workeris notified that they were found to be unreliable, or canceled shiftstoo frequently or without enough notice, or have received poor ratings).In step 612, the platform indicates to the worker user a path to improvetheir ratings by working less restrictive positions and achieving atleast threshold ratings until their overall rating has improved. In step614, the platform delivers reminders regarding behavior to the workeruser prior to their accepted gigs.

FIG. 7 is a flowchart illustrating generation of a potential career pathdevelopmental plan. A near-miss match is a single step in development.However, the platform additionally develops plans for worker users thatoccur over multiple steps. In step 702, the worker user sets a goal. Thegoal is a selected position/gig that they do not qualify for. The goalmay be a currently pending service request that the worker does notmatch with, or may correspond to a template (e.g., a theoreticalposition that an employer user may request in the future).

In step 704, the platform compares the worker user's profile to therequirements of the target. In step 706, the platform generates a seriesof actions, that if taken, would enable the target to be a match for theworker. The series includes a number of (lesser) positions/gigs that theworker user may sign up for in order to develop the required skills andat the required proficiency in those skills.

In step 708, the platform generates and delivers a graphic userinterface that keeps track of the worker user's progress toward thegoal. The user interface may display development with graphs ornumerical statistics. The tracked progress is gamified in the sense thatworker users are encouraged by seeing a graphical representation of theprogress toward their goal.

Automated Resume Builder

FIG. 8 is a flowchart illustrating a process of automatically generatinga resume for workers on the temporary employment platform. The temporaryemployment platform keeps track of jobs worked by any given individual,and thus has all the necessary information to develop a resumeautomatically for the user. Other automated resume builder applicationsinvolve full entry of all relevant details and merely provide formattingservices.

In step 802, a worker signs up for the temporary employment platform.Relevant personal information is given to the platform in order toestablish payroll and other known employment particulars.

In step 804, the worker user engages with the employment platform andworks temporary gigs/shifts for various available positions. Theplatform keeps track of each gig worked, and the relevant skills taggedfrom those gigs (via templates or modified templates for the positionedthe worker filled).

In step 806, the platform receives a request to generate a resume. Instep 808, the platform assembles all positions the worker has performedin, and skills that the worker has and identifies a resume format. Theformat of the resume is based on the worker's profile within theplatform. The resume format/template is automatically determined by theplatform based on the worker's experience, skills, employer reviews, andcertifications. Where a given worker has taken many positions, with manydifferent employers (as measured by threshold values), the resumeformats to focus on the skills the worker has as opposed to individualemployment. Where a worker has a specialized skill (e.g., welding), theresume format is tailored toward employment that is related to thatskill and deemphasizes unrelated employment (e.g., where the worker is awelder, gigs as catering staff are not particularly relevant).

Where a given worker has taken many (as measured by thresholds) shiftsrepeatedly with a given employer user, the resume format focuses on theworker's relationship with the given employer. In some embodiments, ifthe worker user has numerous positive text reviews or score-basedreviews, the resume format focuses on the worker's references andreviews. Positive text reviews are identified via a sentiment analysisengine and semantic construction engine. Sentiment analysis identifieskeywords that indicate sentiment, and automatic semantic analysisattaches that sentiment to specific actions, skills, or performancerelated attributes. Based on the automated analysis, the systemprocedurally generates resume content (e.g., descriptions of skills orattributes). Score-based reviews may easily translate into highlightedcandidate attributes on a resume (e.g., via a hierarchy of attributesand threshold scores that translate into procedurally generated resumeattributes).

The platform includes multiple formats that focus on emphasizing thebest parts of the user's profile or target industries. Additional formattemplates can be added to the platform based on evolving styles invarious industries.

In some embodiments, template styles are identified via machine learningmodels rather than heuristic guidelines (described above). To train themodel, a large plurality of resumes may be input into the system, andthe model interprets what appears on those resumes and correlatescontent of resume (e.g., experiences, skills) to structure (e.g., howthe resume is written). The interpretation is performed via NLP. Themachine learning model uses the user's profile as compared to the datastructures based on the training data to identify what elements of theuser's profile are the most relevant.

Similarly, the model may identify what information tends to not appeartogether and filter lower incidence data out (e.g., when a worker hasmultiple references to experience performing skilled labor tasks, thenexperience at unskilled labor will tend not to appear on the sameresume). In the example, the “lower incidence” data is the experiencerelating to unskilled work; however, the lower incidence varies based onthe training data in the machine learning model. The relevant work datathat is filtered out is based on the resume content that tends to appeartogether across the training set. The population of the training set maybe configured to be biased toward particular traits (e.g., skilled work)by including more instances of skilled work resumes than non-skilledwork.

In some embodiments, especially some embodiments relying upon a machinelearning model, a specific template format is not used. Rather, themachine learning model identifies a path in an artificial neural networkwhere the generated resume content adheres to certain traits or rulesthat are template-like in nature according to that path of the neuralnetwork.

In step 810, the platform automatically fills in fields/page space ofthe resume template identified in step 808. Details used to fill in thetemplate fields favor more recent employment, or employment withstrongest employer reviews (as identified via NLP sentiment analysis).Embodiments of reviews by past employers include star rankings, and/orshort text reviews. In some embodiments, maintenance of a worker profileincludes NLP of the text reviews. The NLP process identifies commonphrases and/or words used in reviews to perform sentiment analysis. Insome embodiments, positive or negative reviews may affect a workerprofile's proficiency statistics.

In some embodiments, resume content may be generated via the machinelearning model constructed using a plurality of resumes as trainingdata. Notably, sufficiency as training data requires a significantnumber of examples. Two resumes alone do not sufficiency constitutetraining data. In many cases, the amount of examples comprising thetraining data correlates to the accuracy of the model. Having a greaternumber of example resumes in the training data will lead to morerealistic looking model generated resume content. However, at some pointadditional examples in the training data have diminishing returns withrespect to model accuracy.

In some embodiments, resume content is generated via procedural rulesand predefined template structures.

In step 812, the user is provided the opportunity to make direct edits,and/or provide feedback to the platform. In some embodiments, new ormodified resume templates are constructed based on user feedbackevaluated by a machine learning model.

Gamification of Temporary Employment Platform

Gamification of the platform refers to providing additional incentives(beyond wages) to encourage behavior on the platform. The behaviorencouraged may be both performance in gigs, as well as actions that aidoperation of the platform itself. Performing certain encouraged behaviorearns a user badges or merits. The badges earn administrative privilegeswithin the system or non-monetary prizes. Examples of the incentivizedbehavior include:

-   -   A. Having worked gigs on X consecutive days. Where X varies in        magnitude and the number of style of badges earned directly        corresponds to the magnitude.    -   B. Earning X consecutive high (e.g., 5/5 star) ratings from        employer users.    -   C. Referring X reliable (e.g., average of 3/5+ star ratings        after a predetermined threshold number of gigs worked) worker        users to the system.    -   D. Maintaining a minimum predetermined rating across multiple        employer users over a predetermined timeframe.    -   E. Achieving a predetermined progression in a given skill (e.g.,        apprentice, journeyman, master).    -   F. Showing up on time to X consecutive gigs as promised (as        evaluated by location data from a GPS on a mobile device        associated with the user as compared to a geofence placed around        the location of the gig at the time of the gig).

Instances of X vary in magnitude and the number of style of badgesearned directly corresponds to that magnitude.

FIG. 9 is a flowchart illustrating a process of gamification of platformengagement. In step 902, a worker user interacts with the platform.Interaction with the platform is qualified as any activity the platformtracks or keeps statistics on. Examples of tracked statistics include:events affecting associate reliability/rating (permissionable),work/payment history (filterable, totalable), relationship management(lifetime hours, most recent customers, match xx % jobs),birthday/anniversary, current assignments, job tickets, job matching,profile updates, view jobs history/decline job history, call offhistory, no show history, preferences, status updates (available/notavailable), and % match to current job.

In step 904, the platform evaluates recorded behavior for candidacy forbadges.

In step 906, the platform awards a user badges to the user profile basedon the tracked behavior. In some embodiments, badges are publiclyvisible on a workers' profile. Badges enable a worker user to haveadditional permissions in the platform or are exchangeable for rewards(e.g., credit toward goods or services).

Badges may be of different types. The types break into separatecategories. First, what type of incentive is included, and second, thedegree of expendability of the badge. A given badge may have anycombination of available incentives, but only a single degree ofexpendability. The types of incentive are privilege related, and rewardrelated (e.g., credit toward goods or services). The degrees ofexpendability are permanent, temporary, or fungible. A permanent badgeis one that once earned is held forever. A temporary badge is one thatexists for a predetermined period or as long as the worker usersatisfies badge requirements, and fungible is a badge that is spent toobtain a reward. In some embodiments, the application badges aredisplayed to users via the graphic user interface with graphic icons.

Example rewards include gifts such as pre-paid vacations, electronics,fruit baskets, etc. Example administrative privileges include theability to invite other, un-verified workers to a given gig (if there isavailability for that gig). Another administrative privilege is theability to un-publish employer reviews (an un-published review isremoved from the worker's profile for purposes other than thoseconnected to the reviewer themselves). A third example of anadministrative privilege is the degree to which a worker is able toengage with the platform and review employers.

In circumstances where the worker user receives an administrativeprivilege as an incentive, issuance of the privilege is automatic. Theuser's graphic user interface changes to include additional controls. Insome embodiments, the user is informed of the change via a notification.

The modification of the graphic user interface may include the inclusionof a new button. When changes to the number or type of badges occur(either addition or subtraction of badges) the application reevaluateswhich application functions the current user has available. In responseto that evaluation the application badges the GUI is modified to includea previously unincluded control that activates the application functionthat was newly added (or revoked) to the user account based on thebadges.

-   -   activating an application function of the application, via a the        previously unincluded control included in the graphic user        interface, for the first user

FIG. 10 is a flowchart illustrating issuance of gamified rewards relatedto badges. In step 1002, a worker user enters a marketplace userinterface. In step 1004, the user selects items and exchanges badges forrewards.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps (or employsystems having blocks) in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide sub- or alternative combinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallelor may be performed at different times. Further, any specific numbersnoted herein are only examples; alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

All patents, applications, and references noted above, including anythat may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further embodiments of thedisclosure.

These and other changes can be made to the disclosure in light of theabove Detailed Description. While the above description describescertain embodiments of the disclosure, and describes the best modecontemplated, no matter how detailed the above appears in text, theteachings can be practiced in many ways. Details of the system may varyconsiderably in its implementation details, while still beingencompassed by the subject matter disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the disclosure should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the disclosure with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the disclosure to the specific embodimentsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe disclosure encompasses not only the disclosed embodiments, but alsoall equivalent ways of practicing or implementing the disclosure underthe claims.

While certain aspects of the disclosure are presented below in certainclaim forms, the inventors contemplate the various aspects of thedisclosure in any number of claim forms. For example, while only oneaspect of the disclosure is recited as a means-plus-function claim under35 U.S.C. § 112, ¶6, other aspects may likewise be embodied as ameans-plus-function claim, or in other forms, such as being embodied ina computer-readable medium. (Any claims intended to be treated under 35U.S.C. § 112, ¶6 will begin with the words “means for.”) Accordingly,the applicant reserves the right to add additional claims after filingthe application to pursue such additional claim forms for other aspectsof the disclosure.

1. A method of guiding an application userbase through application usecomprising: executing an application that matches users to tasks basedon a set of requirements associated with each task and respective userprofiles, wherein a match between a first user and a first task;identifying a near-miss task associated with the first user, wherein thenear-miss task is a task that does not match the first user but wouldmatch but-for a missing user profile criterion; comparing, by theapplication based on monitoring the first user, a first metricindicative of the missing user profile criterion of the first user to asecond metric indicative of the missing user profile criterion, thesecond metric being stored in a worker performance database,automatically determining, by the application based on the comparing,whether remedying the second metric causes the near-miss task to matchfor the first user; displaying the near-miss task and the missing userprofile criterion to the first user via an instance of the applicationexecuting on a user device; and displaying, based on the determining,one or more recommendations that enables the near-miss task to match forthe first user.
 2. The method of claim 1, further comprising:identifying a type of the missing user profile criterion based on afield from which the missing user profile criterion is found in the userprofile of the first user; and generating a development plan based onthe type of the missing user profile criterion, wherein the developmentplan describes a list of actions the first user may take to cause thenear-miss task to become the match for the first user.
 3. The method ofclaim 2, wherein the type of the missing user profile criterion may beany of: skill based; preference based; certification based; orbehavioral.
 4. The method of claim 1, further comprising: tracking taskscompleted by the first user, wherein each task is associated with a setof tracked skills, and wherein completion of each task increases acounter for each tracked skill associated with the respective completedtask; and reevaluating matches based on increases in the counter foreach tracked skill.
 5. The method of claim 2, wherein the type ispreference based, the method further comprising: transmitting a messageto the first user to modify preferences in the user profile of the firstuser.
 6. The method of claim 2, wherein the type is skill based and thedevelopment plan includes a list of tasks that, once performed,qualifies the near-miss task to match for the first user.
 7. The methodof claim 2, wherein the type is certification based and the developmentplan includes instructions describing how to obtain a necessarycertification that qualifies the near-miss task to match for the firstuser.
 8. The method of claim 2, wherein the type is behavioral and thedevelopment plan includes instructions describing a set of user ratingsthe first user requires while performing other tasks that qualifies thenear-miss task to match for the first user.
 9. A system of guiding anapplication userbase through application use comprising: an applicationserver including a memory including instructions that cause theapplication server to host an application that matches users to tasksbased on a set of requirements associated with each task and respectiveuser profiles, wherein a match between a first user and a first task;wherein the memory further includes instructions to identify a near-misstask associated with the first user, wherein the near-miss task is atask that does not match the first user but would match but-for amissing user profile criterion; and a client application configured toexecute on a user device and display the near-miss task and the missinguser profile criterion to the first user via an instance of theapplication executing on the user device, wherein the application serveris further caused to: compare, by the application based on monitoringthe first user, a first metric indicative of the missing user profilecriterion of the first user to a second metric indicative of the missinguser profile criterion, the second metric being stored in a workerperformance database, automatically determine, by the application basedon the comparing, whether remedying the second metric causes thenear-miss task to match for the first user; display the near-miss taskand the missing user profile criterion to the first user via theinstance of the application executing on the user device; and display,based on the determining, one or more recommendations that enables thenear-miss task to match for the first user.
 10. The system of claim 9,wherein the memory further includes instructions to: identify a type ofthe missing user profile criterion based on a field from which themissing user profile criterion is found in the user profile of the firstuser; and generate a development plan based on the type of the missinguser profile criterion, wherein the development plan describes a list ofactions the first user may take to cause the near-miss task to becomethe match for the user.
 11. The system of claim 10, wherein the type ofthe missing user profile criterion may be any of: skill based;preference based; certification based; or behavioral.
 12. The system ofclaim 9, wherein the memory further includes instructions to: tracktasks completed by the first user, wherein each task is associated witha set of tracked skills, and wherein completion of each task increases acounter for each tracked skill associated with the respective completedtask; and reevaluate matches based on increases in the counter for eachtracked skill.
 13. The system of claim 10, wherein the type ispreference based and the memory further includes instructions to:transmit a message to the first user via the client application tomodify preferences in the user profile of the first user.
 14. The systemof claim 10, wherein the type is skill based and the development planincludes a list of tasks that, once performed, qualifies the near-misstask to match for the first user.
 15. The system of claim 10, whereinthe type is certification based and the development plan includesinstructions describing how to obtain a necessary certification thatqualifies the near-miss task to match for the first user.
 16. The systemof claim 10, wherein the type is behavioral and the development planincludes instructions describing a set of user ratings the first userrequires while performing other tasks that qualifies the near-miss taskto match for the first user.
 17. A method comprising: executing anapplication that matches users to tasks using based on a set ofrequirements associated with each task and respective user profiles, thetasks are categorized based on work experience associated therewith, therespective user profiles including a first tracked experience type;identifying a near-miss task associated with a first user, wherein thenear-miss task is a task that does not match the first user but wouldmatch but-for the first user having less experience in the first trackedexperience type than described in the set of requirements of that task,and wherein the near-miss task includes a criterion from a user profileof the first user that, when modified, causes the near-miss task tobecome a match for the first user; identifying a set of tasks that arecategorized into work experience of the first tracked experience type;in response to completion of the set of tasks by the first user, raisingthe first tracked experience type of the first user to meet a describedamount of experience in the first tracked experience type described inthe set of requirements of the near-miss task; and displaying thenear-miss task and the set of tasks to the first user via an instance ofthe application executing on a user device; and displaying, based on theraising, one or more recommendations that enables the near-miss task tomatch for the first user.
 18. The method of claim 17, wherein the userprofile of the first user indicates an amount of the first trackedexperience type, and the amount is above a minimum threshold defined inthe set of requirements of the near-miss task.
 19. The method of claim17, wherein said displaying the set of tasks occurs over a series ofnotifications as tasks become available on the application.
 20. Themethod of claim 17, wherein the set of tasks includes at least onescheduled task and at least one unscheduled task.